language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
To Use This Model
STEP 1:
- Installs Unsloth, Xformers (Flash Attention) and all other packages! according to your environments and GPU
- To install Unsloth on your own computer, follow the installation instructions on our Github page : LINK IS HERE
Now Follow the CODE
markdown
from unsloth import FastLanguageModel
import torch max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally! dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+ load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False. from transformers import AutoTokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="DipeshChaudhary/ShareGPTChatBot-Counselchat1", # Your fine-tuned model
max_seq_length=max_seq_length,
dtype=dtype,
load_in_4bit=load_in_4bit,
)
#We now use the Llama-3 format for conversation style finetunes. We use Open Assistant conversations in ShareGPT style. **We use our get_chat_template function to get the correct chat template. They support zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old and their own optimized unsloth template** from unsloth.chat_templates import get_chat_template tokenizer = get_chat_template( tokenizer, chat_template = "llama-3", # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth mapping = {"role" : "from", "content" : "value", "user" : "human", "assistant" : "gpt"}, # ShareGPT style )
Uploaded model
- Developed by: DipeshChaudhary
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
